内容感知的可解释性路面病害检测模型  

Content-Aware Explainable Pavement Distress Detection Model

作　　者：李傲 葛永新[1] 刘慧君[3] 杨春华 周修庄 Li Ao;Ge Yongxin;Liu Huijun;Yang Chunhua;Zhou Xiuzhuang(School of Big Data&Software Engineering,Chongqing University,Chongqing 401331;School of Intelligent Engineering,Chongqing City Management College,Chongqing 401331;College of Computer Science,Chongqing University,Chongqing 400044;School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing 100876)

机构地区：[1]重庆大学大数据与软件学院,重庆401331 [2]重庆城市管理职业学院智能工程学院,重庆401331 [3]重庆大学计算机学院,重庆400044 [4]北京邮电大学人工智能学院,北京100876

出　　处：《计算机研究与发展》2024年第3期701-715,共15页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(62176031,61972046);重庆市自然科学基金项目(CSTB2022NSCQ-MSX1405);重庆市技术创新与应用发展专项项目(CSTB2022TIAD-KPX0100)。

摘　　要：针对实际场景中高分辨路面图像难以直接作为现有卷积神经网络(convolutional neural network,CNN)的输入、现有预处理及下采样算法无法有效感知并保留原始路面图像中低占比的病害区域信息等问题,借助于可视化解释的技术手段,设计了一种即插即用的图像内容自适应感知模块(adaptive perception module,APM),既平衡了高分辨路面图像与CNN输入限制,又能够自适应感知激活前景病害区域,从而实现高分辨路面图像中病害类型的快速准确检测,构建可信路面病害视觉检测软件系统.APM利用大卷积核和下采样残差操作降低原始图像分辨率并获取图像浅层特征表示;通过注意力机制自适应感知并激活图像中路面病害区域信息,过滤无关的背景信息.利用联合学习的方式,无需额外监督信息完成对APM的训练.通过可视化解释方法辅助选择和设计APM的具体结构,在最新公开数据集CQUBPMDD上的实验结果表明:APM相比于现有的图像预处理采样算法均有明显提升,分类准确率最高为84.47%;在CQU-BPDD上的实验结果及APM决策效果可视化分析表明APM具备良好的泛化性与鲁棒性.实验代码已开源:https://github.com/Li-Ao-Git/apm.To address the challenges of using high-resolution pavement images as input for existing convolutional neural network models and the inability of existing preprocessing algorithms to effectively perceive and retain information from low-ratio distress regions in original pavement images,a novel architectural unit called adaptive perception module(APM)paying greater attention to pavement distress region is proposed with the help of visual interpretation techniques,which achieves a rapid and accurate detection of pavement distress in high-resolution images and could be used to build a software system for automatic detection of pavement distress based on computer vision.Firstly,big kernel convolution and residual operations are used to reduce the origin image resolution and get the low-level but rich feature representation.Secondly,attention mechanism is developed to perceive and activate the region of pavement distress and filter the irrelevant background pixel noise.By means of joint learning,APM training could be completed without additional cost.After the visual interpretation method is used to aid the selection and design of the specific structure of APM,experimental results on the latest public dataset CQU-BPMDD show that the proposed APM significantly improves the classification accuracy,up to 84.47%.Experiments across different datasets CQU-BPDD demonstrate the generalization and robustness of APM.Code is available on https://github.com/Li-Ao-Git/apm.

关 键 词：路面病害检测 可解释性 自适应感知 注意力机制 联合学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]